TY - JOUR
T1 - Model-based diagnosis of special causes in statistical process control
AU - Dooley, K.
AU - Anderson, J.
AU - Liu, X.
PY - 1997/6
Y1 - 1997/6
N2 - Industry has recognized that effective use of automated diagnostic software can greatly enhance process quality and productivity. Simultaneously, significant advances have been made in the technologies of process modelling, using techniques such as neural networks, regression methods, and various analytical approaches. Here we will present a simple method to perform model-based diagnosis. The method is simple to implement, intuitively appealing, and requires information that should be standardly available. The method requires as input current process data, set-point information, and a predictive process model, and outputs a table of diagnostic scores which indicate the likelihood of a particular factor being the cause of an observed special cause on a statistical process control chart.
AB - Industry has recognized that effective use of automated diagnostic software can greatly enhance process quality and productivity. Simultaneously, significant advances have been made in the technologies of process modelling, using techniques such as neural networks, regression methods, and various analytical approaches. Here we will present a simple method to perform model-based diagnosis. The method is simple to implement, intuitively appealing, and requires information that should be standardly available. The method requires as input current process data, set-point information, and a predictive process model, and outputs a table of diagnostic scores which indicate the likelihood of a particular factor being the cause of an observed special cause on a statistical process control chart.
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U2 - 10.1080/002075497195155
DO - 10.1080/002075497195155
M3 - Article
AN - SCOPUS:0031167857
SN - 0020-7543
VL - 35
SP - 1609
EP - 1616
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 6
ER -